How Utilities Are Rethinking Resiliency With Ai Planning …
Electric infrastructure is aging, climate threats are becoming more prevalent, and the U.S. grid is rapidly adding new load for the first time in decades. It’s more important than ever for utilities to prioritize the right investments for their systems, but how do they know which ones will pay off?
That’s a good question for Mishal Thadani, co-founder and chief executive officer of Rhizome, an artificial intelligence (AI)-powered software platform that quantifies the economic and social impacts of infrastructure investments in the name of enhancing climate resilience. Thadani and his team leverage high-resolution intelligence to construct accurate, asset-level risk models that lead to defensible utility investment plans and more reliable systems. Using machine learning, the platform analyzes geographic, building, and system data, identifying potential asset failures and helping utilities National Grid quantify wildfire risk.
Thadani recently connected with Factor This to answer the pressing query posed above, as well as several others. Below, you’ll find his thoughts on some of the hottest topics keeping utility planners up at night and learn how software Rhizome’s is making a major difference.
How are utilities thinking about resiliency, and is it different from how it has been traditionally? What prompted that evolution?
Mishal: Utilities have shifted from a largely reactive, compliance-driven mindset towards a proactive, data-driven, forward-looking approach. Traditionally, reactive measures looked inspecting assets (poles, transformers, relays, etc.), replacing the ones that they find are in poor condition, and large recovery efforts after major events. Now, utilities are increasingly aware that climate change-fueled extreme weather events are necessitating a fundamentally different planning mindset.
This evolution is driven not just by a degradation of utility reliability performance, driven in large part by more frequent and intense weather events, but also by unprecedented wildfire risk outside of traditional wildfire zones. Regulatory pressure and the need to justify grid investments to regulators and ratepayers under tighter budgets are also contributing to this evolution. Tools that quantify risks over decades and present multiple dynamic scenarios are enabling utilities to transition from disjointed, stand-alone grid-hardening projects to integrated resilience strategies. These strategies allow utilities to properly consider and weigh future climate impacts alongside resilience investment costs and reliability.
When utilities are planning years out, what sort of things are they keeping in mind? How important is flexibility in those plans?
Long-range resilience planning includes the age and health of their assets, climate models, changing load patterns and projections, regulatory and policy objectives, the presence of evolving threats, wildfire risk moving into new regions, which parts of their system are most vulnerable to failure, and how consequential those failures would be. Given the dynamic nature of these variables and threats, flexibility is essential. Resilience plans must accommodate uncertainty throughout extreme weather scenario analysis, grid updates, and investment prioritization. This means that instead of designing billion-dollar plans based on historical trends and hoping those trends will hold for decades, utilities are modeling various risk scenarios and identifying how resilience investments perform across them.
How do utility resiliency efforts positively impact affordability for customers?
Proactive planning helps avoid emergency repairs, which are almost always more expensive than planned upgrades. But the affordability case is sharper than that. Wildfire-related costs are becoming a meaningful of utility revenue requirements, and when those costs arrive reactively, they translate into rate increases that are harder to manage and harder to justify to regulators and ratepayers. In some cases, wildfire-related costs have been a significant driver of rate hike filings exceeding 25%.
When utilities can model risk scenarios in advance and target investments precisely, they’re spending capital where it has the highest impact rather than spreading it thin across the system or absorbing emergency costs after the fact. That precision helps keep rate impacts more stable and more defensible in regulatory proceedings.
What sort of resiliency upgrades provide an immediate “bang for the buck” for utilities? Which ones take longer to pay off?
Most risk-mitigating strategies work well; it’s more about how much to invest in a program and in what locations. For instance, our modeling has shown that for one utility in the Northeast, vegetation management (trimming trees along the power line’s right-of-way) is most effective on a 6-year cycle, but returns diminish for a 4-year cycle, and even more so on a 2-year cycle. Locationally, based on tree regrowth, this might vary.
System-wide hardening measures (for example: wire undergrounding), such as undergrounding or rebuilding overhead lines, requires much more time to design, permit, and execute on the project, though provide immediate benefits once they’re complete. The investments that result in the greatest “bang for the buck” are the ones that provide benefits across many value streams, such as blue sky reliability improvement, reduction in extreme weather-related failures and impacts, reduction in wildfire risk, and also help reduce the cost of maintaining the assets.
When evaluating upgrades, utilities weigh:
- Climate models, which show the lihood and severity of impacts over decades
- The fragility of their assets against various hazards
- The critical nature of specific grid assets to reliability and safety
- Detailed cost-benefit analyses
- Regulatory standards and considerations
- Rate impacts
Which utilities or groups are pioneering in this space, and how? Can you a case study/example?
National Grid is a strong example of a utility company that’s pioneering in the resilience space. They recently partnered with Rhizome to integrate advanced AI modeling to proactively manage wildfire mitigation planning across its Northeastern U.S. and UK networks, even though these risks aren’t as high as in the West. They’ve also been studying the future climate hazards and have filed forward-looking resilience plans in both New York and Massachusetts. Our models found that, while their overall risk is low, the risk they do have is concentrated in a very small part of their system, where they can now precisely execute hardening measures to reduce that potential risk.
CenterPoint is another example. Their $2.7B resilience plan was approved, and they’ve been executing on a portfolio of technology companies to build a digital twin of their system and planning hardening measures with precision, including using new materials for poles.
CenterPoint has installed 7,000 ‘storm resilient’ poles since Hurricane Beryl
How do increasingly frequent and severe weather events, including wildfires in places where they previously weren’t a problem, change the equation?
Climate change is moving risk into regions that haven’t historically planned for it. Wildfire risk is a clear example – it’s no longer only a problem in the Western U.S. In 2024, New York and Massachusetts saw more than double the wildfire incidents of the previous year, and late-year fires across New York, New Jersey, Massachusetts, and Connecticut burned more than 8,000 acres. These aren’t regions with decades of wildfire planning infrastructure in place.
Nationally, wildfire acreage burned in 2024 jumped 231% over 2023. And while the acreage in the Northeast is still lower than that in the West in absolute terms, the risk profile is different. These are densely populated states with much older infrastructure. A 500-acre fire in rural Massachusetts doesn’t look one in rural Idaho. The proximity of grid assets to people and structures means the consequence per acre of a fire is significantly higher.
Legacy planning models aren’t built for that kind of geographic shift. Utilities in these areas need forward-looking, probabilistic risk projections, not extrapolations from historical averages that no longer reflect what’s coming.
How have technological advancements (i.e., drones, AI, digital twins) changed the game for utilities?
AI, digital twins, drones, and advanced climate analytics are enabling utilities to organize and process massive datasets. Utilities can take weather patterns, climate models, and asset conditions, and use them to quantify and compare risks and associated investments. These technologies are allowing utilities to shift from broad-based programs toward high-fidelity precision.
When it comes to resilience strategies utilities can defend, how much of that is about using data to proactively get ahead of issues rather than reactively respond to them?
It’s almost entirely about getting ahead of it, and regulators are increasingly requiring it. Fourteen states, including California, Texas, Florida, and New York, now have formal resilience plan requirements for regulated utilities, and at least 30 utilities have filed plans. These filings need to be grounded in data, not narratives. Regulators want to see climate projections, asset condition analysis, and quantified cost-benefit ratios.
The utility sector faces an estimated $500 billion resilience funding gap. In 2024 alone, the U.S. experienced 27 billion-dollar weather and climate disasters costing $182.7 billion. The utilities building the most defensible strategies right now are the ones grounding their plans in forward-looking data, including climate projections, asset vulnerability modeling, and simulated failure scenarios, rather than waiting for the next major event to reveal where the weaknesses were.
What lessons are utilities learning from recent inclement weather events/natural disasters that have tested grid resiliency?
The biggest lesson from recent major weather events is that hardening alone doesn’t get you there. Wildfire ignitions alone have cost the utility industry more than $100 billion in the last decade, according to industry estimates, and those numbers don’t account for the cascading effects on rate cases, credit ratings, and public trust. The financial exposure is real and growing faster than most planning frameworks were designed to handle.
Utilities that pair physical upgrades with data-driven planning are recovering faster and with fewer cascading failures. System-wide situational awareness, pre-prioritized response actions, and scenario modeling all contribute to that. In our work with a Texas utility, our models captured 72% more potential asset failures than their previous approach, providing significantly greater precision in targeting interventions before problems occur.
One of the things we see consistently is that failures in one part of the grid compound quickly when utilities don’t have visibility into interdependencies. The utilities that have modeled against a range of scenarios before the event hits are in a fundamentally better position to adapt when conditions on the ground don’t match the plan.
About the Author
Mishal Thadani is the co-founder and CEO of Rhizome, a climate resilience software company that helps utilities identify vulnerabilities from extreme weather threats and optimize resilience investments.
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Renewableenergyworld.com